Matrix heatmaps

Visualize the Z-transformed abundance matrix

# Plot the abundances heatmap
heat(melt(abundances(pseqz)), "Var1", "Var2", "value")

# Plot the abundances heatmap
heat(melt(abundances(pseqz)), "Var1", "Var2", "value")

# Plot the abundances heatmap
heat(melt(abundances(pseqz)), "Var1", "Var2", "value")

Find visually appealing order for rows and columns with the Neatmap approach:

# Sort the matrix rows and cols directly
xo <- neat(abundances(pseqz), method = "NMDS", distance = "euclidean") 

# Heatmap visualization
heat(melt(xo), "Var1", "Var2", "value")

# or use a shortcut to sorting rows (or columns) if just the order was needed 
sorted.rows <- neatsort(abundances(pseqz), "rows", method = "NMDS", distance = "euclidean") 

# Sort the matrix rows and cols directly
xo <- neat(abundances(pseqz), method = "NMDS", distance = "euclidean") 

# Heatmap visualization
heat(melt(xo), "Var1", "Var2", "value")

# or use a shortcut to sorting rows (or columns) if just the order was needed 
sorted.rows <- neatsort(abundances(pseqz), "rows", method = "NMDS", distance = "euclidean") 

# Sort the matrix rows and cols directly
xo <- neat(abundances(pseqz), method = "NMDS", distance = "euclidean") 

# Heatmap visualization
heat(melt(xo), "Var1", "Var2", "value")

# or use a shortcut to sorting rows (or columns) if just the order was needed 
sorted.rows <- neatsort(abundances(pseqz), "rows", method = "NMDS", distance = "euclidean") 

Cross-correlating data sets

Cross-correlate columns of two data sets from related to microbiome and blood serum lipids associations (PeerJ 1:e32).

The function returns correlations, raw p-values, and fdr estimates (not strictly proper as the comparisons are not independent). Keep only those elements that have at least only one significant correlation (n.signif):

# Load example data 
otu <- peerj32$microbes 
lipids <- peerj32$lipids 

# Define data sets to cross-correlate
x <- log10(otu) # OTU Log10 (44 samples x 130 genera)
y <- as.matrix(lipids) # Lipids (44 samples x 389 lipids)

# Cross correlate data sets
correlations <- associate(x, y, method = "spearman", mode = "matrix", p.adj.threshold = 0.05, n.signif = 1)

# Or, alternatively, the same output is also available in a handy table format
correlation.table <- associate(x, y, method = "spearman", mode = "table", p.adj.threshold = 0.05, n.signif = 1)

kable(head(correlation.table))

Association heatmaps

Rearrange the data and plot the heatmap and mark significant correlations with stars to reproduce microbiota-lipidome heatmap from Lahti et al. PeerJ (2013) (the ordering of rows and columns may be different):

p <- heat(correlation.table, "X1", "X2", fill = "Correlation", star = "p.adj", p.adj.threshold = 0.05) 

print(p)

Rearrange the data and plot the heatmap and mark significant correlations with stars to reproduce microbiota-lipidome heatmap from Lahti et al. PeerJ (2013) (the ordering of rows and columns may be different):

p <- heat(correlation.table, "X1", "X2", fill = "Correlation", star = "p.adj", p.adj.threshold = 0.05) 

print(p)

Rearrange the data and plot the heatmap and mark significant correlations with stars to reproduce microbiota-lipidome heatmap from Lahti et al. PeerJ (2013) (the ordering of rows and columns may be different):

p <- heat(correlation.table, "X1", "X2", fill = "Correlation", star = "p.adj", p.adj.threshold = 0.05) 

print(p)

Heatmaps with ggplot2

The above examples provide handy shortcuts for heatmap visualization. You can also directly modify the ggplot2 routines. This time, let us set q-value threshold also for cell coloring:

# Order the rows and columns with levels argument if needed:
correlation.table$X1 <- factor(correlation.table$X1, levels = unique(as.character(correlation.table$X1)))
correlation.table$X2 <- factor(correlation.table$X2, levels = unique(as.character(correlation.table$X2)))

# Set black-and-white theme
library(ggplot2)
theme_set(theme_bw())

# Pick only the correlations with q<0.05
# Note: this will leave other cells empty
library(dplyr)
subtable <- filter(correlation.table, p.adj < 0.05)

# Arrange the figure
p <- ggplot(subtable, aes(x = X1, y = X2, fill = Correlation))
p <- p + geom_tile() 
p <- p + scale_fill_gradientn("Correlation", 
                       breaks = seq(from = -1, to = 1, by = 0.2), 
                   colours = c("darkblue", "blue", "white", "red", "darkred"), 
                   limits = c(-1,1)) 

# Polish texts
p <- p + theme(axis.text.x=element_text(angle = 90))
p <- p + xlab("") + ylab("")

# Mark the most significant cells with stars
p <- p + geom_text(data = subset(correlation.table, p.adj < 0.02), 
               aes(x = X1, y = X2, label = "+"), col = "white", size = 5)

# Plot
print(p)

Heatmap with text

For detailed information, might be handy to print the actual values on top of the heatmap:

theme_set(theme_bw(20))
df <- correlation.table
p <- ggplot(df, aes(X1, X2, group=X2)) 
p <- p + geom_tile(aes(fill = Correlation)) 
p <- p + geom_text(aes(fill = df$Correlation, label = round(df$Correlation, 1)), size = 2) 
p <- p + scale_fill_gradientn("Correlation", 
                      breaks = seq(from = -1, to = 1,  by = 0.25), 
                      colours = c("blue", "white", "red"), 
                  limits = c(-1, 1))
p <- p + theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) 
p <- p + xlab("") + ylab("")
print(p)

ggcorr

An alternative way to visualize correlation matrices is provided by the ggcorr package. Note: this toy example does not consider the compositionality effect in microbial abundance correlations. See the package site for more detailed examples and many more options.

library(GGally)
ggcorr(x[, 1:10], method = c("pairwise", "spearman"), nbreaks = 20, hjust = 0.75)
ggcorr(x[, 1:10], method = c("pairwise", "spearman"), nbreaks = 20, geom = "circle")
ggcorr(x[, 1:10], method = c("pairwise", "spearman"), nbreaks = 20, label = TRUE, label_alpha = TRUE)
ggcorr(data = NULL, cor_matrix = cor(x[, 1:10], use = "everything"), low = "steelblue", mid = "white", high = "darkred", midpoint = 0)